Abstract

Abstract Nowadays, applications dealing with volumetric datasets, Medical applications being a typical representative, have become possible even on low cost computers due to a rapid increase of computer memory and processing power. However, even today, dealing with volumetric datasets creates two considerable problems: slow visualization and large file sizes. While recently, due to significant progress in graphics hardware, real-time or near real-time volume visualization has become possible, volume compression still remains a problematic issue. This paper introduces a new method for lossless compression of volumetric datasets. It is based on quadtree encoding. The method consists of three steps: during initialization, so-called division quadtree is built. The smallest unit of the division quadtree is called basic macro-block. During the processing phase, Boolean intersection is built on pairs of quadtrees, and the differences are stored. In the last phase, the variable length encoding is applied to reduce the entropy among the differences. Proposed method supports progressive visualization, what is especially important when a transfer trough the internet is needed. To test the efficiency of this method it was compared to popular octree encoding scheme. The results proved that data coherence is exploited more sufficiently using proposed quadtree approach. Additional advantage of this approach is that the algorithm does not need a lot of memory space. Only two quadtrees of two consecutive slices need be loaded in the memory at the same time. This feature makes this algorithm extremely attractive for possible hardware implementation. This paper introduces a new method for the compression of volumetric datasets. It is based on quadtree encoding. This method consists of three steps: during initialization, a so-called division quadtree is built. The smallest, unit of the division quadtree is called a basic macro-block. A Boolean intersection is built on pairs of quadtrees during the processing phase and the differences are stored. In the last phase, variable length encoding is applied to reduce entropy among the differences. This method has been compared with the popular octree-based method and gives, in general, better compression results. In addition, this method can be realized using small on-board memory.

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